import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	str         =    logging.get_logger(__name__)

set_seed(7_7_0)


__a    :	Optional[Any]         =    {
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


__a    :	Optional[Any]         =    {
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

__a    :	Any         =    os.path.dirname(os.path.abspath(__file__))
__a    :	str         =    os.path.join(os.path.expanduser("""~"""), """.cache""")
__a    :	List[str]         =    os.path.join(os.getenv("""XDG_CACHE_HOME""", default_cache_dir), """suno""", """bark_v0""")





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				model_type
				if use_small:
								key += "_small"
				return os.path.join(lowercase				,     REMOTE_MODEL_PATHS[key]['''file_name''']				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				os.makedirs(lowercase				,     exist_ok=lowercase				)
				hf_hub_download(repo_id=lowercase				,     filename=lowercase				,     local_dir=lowercase				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				,     lowercase="text"				):







				"""simple docstring"""



				if model_type == "text":
								__lowercase					     =				BarkSemanticModel
								__lowercase					     =				BarkSemanticConfig
								__lowercase					     =				BarkSemanticGenerationConfig
				elif model_type == "coarse":
								__lowercase					     =				BarkCoarseModel
								__lowercase					     =				BarkCoarseConfig
								__lowercase					     =				BarkCoarseGenerationConfig
				elif model_type == "fine":
								__lowercase					     =				BarkFineModel
								__lowercase					     =				BarkFineConfig
								__lowercase					     =				BarkFineGenerationConfig
				else:
								raise NotImplementedError()
				__lowercase					     =				F"{model_type}_small" if use_small else model_type
				__lowercase					     =				REMOTE_MODEL_PATHS[model_key]
				if not os.path.exists(lowercase				):
								logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."				)
								_download(model_info['''repo_id''']				,     model_info['''file_name''']				)
				__lowercase					     =				torch.load(lowercase				,     map_location=lowercase				)
				# this is a hack
				__lowercase					     =				checkpoint['''model_args''']
				if "input_vocab_size" not in model_args:
								__lowercase					     =				model_args['''vocab_size''']
								__lowercase					     =				model_args['''vocab_size''']
								del model_args["vocab_size"]

				# convert Bark model arguments to HF Bark model arguments
				__lowercase					     =				model_args.pop('''n_head'''				)
				__lowercase					     =				model_args.pop('''n_embd'''				)
				__lowercase					     =				model_args.pop('''n_layer'''				)

				__lowercase					     =				ConfigClass(**checkpoint['''model_args''']				)
				__lowercase					     =				ModelClass(config=lowercase				)
				__lowercase					     =				GenerationConfigClass()

				__lowercase					     =				model_generation_config
				__lowercase					     =				checkpoint['''model''']
				# fixup checkpoint
				__lowercase					     =				'''_orig_mod.'''
				for k, v in list(state_dict.items()				):
								if k.startswith(lowercase				):
												# replace part of the key with corresponding layer name in HF implementation
												__lowercase					     =				k[len(lowercase				) :]
												for old_layer_name in new_layer_name_dict:
																__lowercase					     =				new_k.replace(lowercase				,     new_layer_name_dict[old_layer_name]				)

												__lowercase					     =				state_dict.pop(lowercase				)

				__lowercase					     =				set(state_dict.keys()				) - set(model.state_dict().keys()				)
				__lowercase					     =				{k for k in extra_keys if not k.endswith('''.attn.bias'''				)}
				__lowercase					     =				set(model.state_dict().keys()				) - set(state_dict.keys()				)
				__lowercase					     =				{k for k in missing_keys if not k.endswith('''.attn.bias'''				)}
				if len(lowercase				) != 0:
								raise ValueError(F"extra keys found: {extra_keys}"				)
				if len(lowercase				) != 0:
								raise ValueError(F"missing keys: {missing_keys}"				)
				model.load_state_dict(lowercase				,     strict=lowercase				)
				__lowercase					     =				model.num_parameters(exclude_embeddings=lowercase				)
				__lowercase					     =				checkpoint['''best_val_loss'''].item()
				logger.info(F"model loaded: {round(n_params/1E6				,     1				)}M params, {round(lowercase				,     3				)} loss"				)
				model.eval()
				model.to(lowercase				)
				del checkpoint, state_dict

				return model





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				,     lowercase="text"				):







				"""simple docstring"""



				if model_type not in ("text", "coarse", "fine"):
								raise NotImplementedError()

				__lowercase					     =				'''cpu'''  # do conversion on cpu

				__lowercase					     =				_get_ckpt_path(lowercase				,     use_small=lowercase				)
				__lowercase					     =				_load_model(lowercase				,     lowercase				,     model_type=lowercase				,     use_small=lowercase				)

				# load bark initial model
				__lowercase					     =				_bark_load_model(lowercase				,     '''cpu'''				,     model_type=lowercase				,     use_small=lowercase				)

				if model_type == "text":
								__lowercase					     =				bark_model['''model''']

				if model.num_parameters(exclude_embeddings=lowercase				) != bark_model.get_num_params():
								raise ValueError('''initial and new models don\'t have the same number of parameters'''				)

				# check if same output as the bark model
				__lowercase					     =				5
				__lowercase					     =				10

				if model_type in ["text", "coarse"]:
								__lowercase					     =				torch.randint(256				,     (batch_size, sequence_length)				,     dtype=torch.int				)
								__lowercase					     =				bark_model(lowercase				)[0]

								__lowercase					     =				model(lowercase				)

								# take last logits
								__lowercase					     =				output_new_model_total.logits[:, [-1], :]

				else:
								__lowercase					     =				3
								__lowercase					     =				8
								__lowercase					     =				torch.randint(256				,     (batch_size, sequence_length, n_codes_total)				,     dtype=torch.int				)

								__lowercase					     =				model(lowercase				,     lowercase				)
								__lowercase					     =				bark_model(lowercase				,     lowercase				)

								__lowercase					     =				output_new_model_total.logits

				# output difference should come from the difference of self-attention implementation design
				if output_new_model.shape != output_old_model.shape:
								raise ValueError('''initial and new outputs don\'t have the same shape'''				)
				if (output_new_model - output_old_model).abs().max().item() > 1E-3:
								raise ValueError('''initial and new outputs are not equal'''				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				model.save_pretrained(lowercase				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				,     ):







				"""simple docstring"""



				__lowercase					     =				os.path.join(lowercase				,     lowercase				)

				__lowercase					     =				BarkSemanticConfig.from_pretrained(os.path.join(lowercase				,     '''config.json'''				)				)
				__lowercase					     =				BarkCoarseConfig.from_pretrained(os.path.join(lowercase				,     '''config.json'''				)				)
				__lowercase					     =				BarkFineConfig.from_pretrained(os.path.join(lowercase				,     '''config.json'''				)				)
				__lowercase					     =				EncodecConfig.from_pretrained('''facebook/encodec_24khz'''				)

				__lowercase					     =				BarkSemanticModel.from_pretrained(lowercase				)
				__lowercase					     =				BarkCoarseModel.from_pretrained(lowercase				)
				__lowercase					     =				BarkFineModel.from_pretrained(lowercase				)
				__lowercase					     =				EncodecModel.from_pretrained('''facebook/encodec_24khz'''				)

				__lowercase					     =				BarkConfig.from_sub_model_configs(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				)

				__lowercase					     =				BarkGenerationConfig.from_sub_model_configs(
				    semantic.generation_config				,     coarseAcoustic.generation_config				,     fineAcoustic.generation_config				)

				__lowercase					     =				BarkModel(lowercase				)

				__lowercase					     =				semantic
				__lowercase					     =				coarseAcoustic
				__lowercase					     =				fineAcoustic
				__lowercase					     =				codec

				__lowercase					     =				bark_generation_config

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				bark.save_pretrained(lowercase				,     repo_id=lowercase				,     push_to_hub=lowercase				)


if __name__ == "__main__":
		__a    :	Any         =    argparse.ArgumentParser()
		# Required parameters

		parser.add_argument("""model_type""", type=str, help="""text, coarse or fine.""")
		parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
		parser.add_argument("""--is_small""", action="""store_true""", help="""convert the small version instead of the large.""")

		__a    :	Tuple         =    parser.parse_args()

		load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Optional[Any]         =    logging.get_logger(__name__)

__a    :	Optional[Any]         =    torch.device("""cpu""")





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				if swiftformer_name == "swiftformer_xs":
								return torch.tensor([-2.1703E00, 2.1107E00, -2.0811E00, 8.8685E-01, 2.4360E-01]				)

				elif swiftformer_name == "swiftformer_s":
								return torch.tensor([3.9636E-01, 2.3478E-01, -1.6963E00, -1.7381E00, -8.6337E-01]				)

				elif swiftformer_name == "swiftformer_l1":
								return torch.tensor([-4.2768E-01, -4.7429E-01, -1.0897E00, -1.0248E00, 3.5523E-02]				)

				elif swiftformer_name == "swiftformer_l3":
								return torch.tensor([-2.5330E-01, 2.4211E-01, -6.0185E-01, -8.2789E-01, -6.0446E-02]				)





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				dct.pop(lowercase				)
				__lowercase					     =				val





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				[]
				for k in state_dict.keys():
								__lowercase					     =				k
								if ".pwconv" in k:
												__lowercase					     =				k_new.replace('''.pwconv'''				,     '''.point_wise_conv'''				)
								if ".dwconv" in k:
												__lowercase					     =				k_new.replace('''.dwconv'''				,     '''.depth_wise_conv'''				)
								if ".Proj." in k:
												__lowercase					     =				k_new.replace('''.Proj.'''				,     '''.proj.'''				)
								if "patch_embed" in k_new:
												__lowercase					     =				k_new.replace('''patch_embed'''				,     '''swiftformer.patch_embed.patch_embedding'''				)
								if "network" in k_new:
												__lowercase					     =				k_new.split('''.'''				)
												if ls[2].isdigit():
																__lowercase					     =				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]				)
												else:
																__lowercase					     =				k_new.replace('''network'''				,     '''swiftformer.encoder.network'''				)
								rename_keys.append((k, k_new)				)
				return rename_keys





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				):







				"""simple docstring"""



				__lowercase					     =				SwiftFormerConfig()

				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				__lowercase					     =				1000
				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				'''imagenet-1k-id2label.json'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				# size of the architecture
				if swiftformer_name == "swiftformer_xs":
								__lowercase					     =				[3, 3, 6, 4]
								__lowercase					     =				[48, 56, 112, 220]

				elif swiftformer_name == "swiftformer_s":
								__lowercase					     =				[3, 3, 9, 6]
								__lowercase					     =				[48, 64, 168, 224]

				elif swiftformer_name == "swiftformer_l1":
								__lowercase					     =				[4, 3, 10, 5]
								__lowercase					     =				[48, 96, 192, 384]

				elif swiftformer_name == "swiftformer_l3":
								__lowercase					     =				[4, 4, 12, 6]
								__lowercase					     =				[64, 128, 320, 512]

				# load state_dict of original model, remove and rename some keys
				if original_ckpt:
								if original_ckpt.startswith('''https'''				):
												__lowercase					     =				torch.hub.load_state_dict_from_url(lowercase				,     map_location='''cpu'''				,     check_hash=lowercase				)
								else:
												__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)
				__lowercase					     =				checkpoint

				__lowercase					     =				create_rename_keys(lowercase				)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(lowercase				,     lowercase				,     lowercase				)

				# load HuggingFace model
				__lowercase					     =				SwiftFormerForImageClassification(lowercase				).eval()
				hf_model.load_state_dict(lowercase				)

				# prepare test inputs
				__lowercase					     =				prepare_img()
				__lowercase					     =				ViTImageProcessor.from_pretrained('''preprocessor_config'''				)
				__lowercase					     =				processor(images=lowercase				,     return_tensors='''pt'''				)

				# compare outputs from both models
				__lowercase					     =				get_expected_output(lowercase				)
				__lowercase					     =				hf_model(inputs['''pixel_values''']				).logits

				assert hf_logits.shape == torch.Size([1, 1000]				)
				assert torch.allclose(hf_logits[0, 0:5]				,     lowercase				,     atol=1E-3				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"				)
				hf_model.save_pretrained(lowercase				)


if __name__ == "__main__":
		__a    :	Optional[int]         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--swiftformer_name""",
		    default="""swiftformer_xs""",
		    choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
		    type=str,
		    help="""Name of the SwiftFormer model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""",
		    default="""./converted_outputs/""",
		    type=str,
		    help="""Path to the output PyTorch model directory.""",
		)
		parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

		__a    :	Optional[int]         =    parser.parse_args()
		convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)